Checks on testing

library(covid19census)
library(dplyr)
library(tidyr)
library(ggplot2)
library(broom)
library(psych)
library(stringr)
library(DataExplorer)
library(plotly)
theme_set(theme_bw())

t_us <-  covid19census::getus_tests()

names(t_us)

t_us %>% 
  select(date, state, positive, negative, pending) %>% 
  pivot_longer(cols = c("positive", "negative", "pending"), names_to = "det_tests") %>% 
  
  ggplot(aes(date, log2(value), colour = det_tests)) +
    geom_line() +
    facet_wrap(vars(state)) +
    scale_x_date(guide = guide_axis(check.overlap = TRUE, angle = 45)) +
    theme(legend.position = "top",
        legend.title = element_blank()) 

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t_us %>%
  select(date,
         state,
         hospitalized_curr,
         hospitalized_cumul
         # icu_curr,
         # icu_cumul
         
         ) %>%
  pivot_longer(
    cols = c(
      "hospitalized_curr",
      "hospitalized_cumul"
      # "icu_curr",
      # "icu_cumul"
    ),
    names_to = "det_tests"
  ) %>%
  
  ggplot(aes(date, log2(value), colour = det_tests)) +
  geom_line() +
  facet_wrap(vars(state)) +
  scale_x_date(guide = guide_axis(check.overlap = TRUE, angle = 45)) +
  theme(legend.position = "top",
        legend.title = element_blank())

Same states have hospitalized current but not hospitalized cumulative and vicevera ????

** Possible different meaning of variables depending on the state **

t_us %>%
  select(date,
         state,
         # hospitalized_curr,
         # hospitalized_cumul
         # icu_curr,
         # icu_cumul
         "ventilator_curr",
         "ventilator_cumul"
         
         ) %>%
  pivot_longer(
    cols = c(
      # "hospitalized_curr",
      # "hospitalized_cumul"
        "ventilator_curr",
         "ventilator_cumul"
    ),
    names_to = "det_tests"
  ) %>%
  
  ggplot(aes(date, log2(value), colour = det_tests)) +
  geom_line() +
  facet_wrap(vars(state)) +
  scale_x_date(guide = guide_axis(check.overlap = TRUE, angle = 45)) +
  theme(legend.position = "top",
        legend.title = element_blank())

t_us %>%
  select(date,
         state,
         # hospitalized_curr,
         # hospitalized_cumul
         icu_curr,
         icu_cumul
         
         ) %>%
  pivot_longer(
    cols = c(
      # "hospitalized_curr",
      # "hospitalized_cumul"
      "icu_curr",
      "icu_cumul"
    ),
    names_to = "det_tests"
  ) %>%
  
  ggplot(aes(date, log2(value), colour = det_tests)) +
  geom_line() +
  facet_wrap(vars(state)) +
  scale_x_date(guide = guide_axis(check.overlap = TRUE, angle = 45)) +
  theme(legend.position = "top",
        legend.title = element_blank())

Lets see some totals

ggplotly(
t_us %>% 
  filter(date == max(date)) %>% 
  select(-hash, -date_checked, -fips) %>%
  pivot_longer(cols = positive:total_test_increase, names_to = "metric", values_to = "value") %>% 
  ggplot(aes(state, value)) +
    geom_col() +
    facet_wrap(vars(metric), scales = "free_y", ncol = 1) + 
    scale_x_discrete(guide = guide_axis( angle = 90)),
    height = 2000,
    width = 600
)

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